New mechanism for archive maintenance in PSO-based multi-objective feature selection

作者：Hoai Bach Nguyen; Xue Bing; Liu Ivy; Andreae Peter; Zhang Mengjie

来源：Soft Computing, 2016, 20(10): 3927-3946.

DOI：10.1007/s00500-016-2128-8

• Multi-objective
• Feature selection
• Classification
• Particle Swarm Optimization

In classification problems, a large number of features are typically used to describe the problem's instances. However, not all of these features are useful for classification. Feature selection is usually an important pre-processing step to overcome the problem of "curse of dimensionality". Feature selection aims to choose a small number of features to achieve similar or better classification performance than using all features. This paper presents a particle swarm Optimization (PSO)-based multi-objective feature selection approach to evolving a set of non-dominated feature subsets which achieve high classification performance. The proposed algorithm uses local search techniques to improve a Pareto front and is compared with a pure multi-objective PSO algorithm, three well-known evolutionary multi-objective algorithms and a current state-of-the-art PSO-based multi-objective feature selection approach. Their performances are examined on 12 benchmark datasets. The experimental results show that in most cases, the proposed multi-objective algorithm generates better Pareto fronts than all other methods.

• 出版日期2016-10